AI Specialist II – Mathematical Optimization / Operations Research
Location: Remote
Duration: : 12+ Months
Interview: Initial call followed by a video interview with the client
Target Start Date: November 30, 2026
Position Overview
Duke Energy is seeking an AI Specialist II with a strong background in Mathematical Optimization, Operations Research, and Python Software Engineering to develop an optimization engine for capital investment planning and prioritization.
This is a math-heavy software engineering role, not a traditional AI or Python development position. The engineer will build decision-support solutions that determine which infrastructure projects should be funded, when they should be executed, and how available budgets should be allocated while satisfying multiple business and operational constraints.
The ideal candidate will have experience developing Mixed-Integer Linear Programming (MILP) models and translating complex business requirements into mathematical optimization solutions. Candidates with backgrounds in Industrial Engineering, Operations Research, Supply Chain Optimization, Logistics, Utility Planning, or Financial Portfolio Optimization are encouraged to apply.
Key Responsibilities
- Design, develop, test, and maintain mathematical optimization models for capital investment planning.
- Formulate and implement Mixed-Integer Linear Programming (MILP) models using Python.
- Translate business rules into mathematical constraints, including budget limits, resource capacity, project dependencies, investment alternatives, and scheduling windows.
- Develop weighted objective functions that evaluate reliability, safety, financial value, risk, and lifecycle benefits.
- Build optimization workflows that support scenario creation, comparison, reruns, and manual overrides.
- Implement deterministic and reproducible optimization processes with versioned inputs and controlled solver settings.
- Develop diagnostic capabilities to identify infeasible models and explain why certain investment scenarios cannot be executed.
- Evaluate and tune optimization solvers based on solution quality, performance, licensing cost, and scalability.
- Integrate predictive analytics outputs, including Probability of Failure (PoF), Remaining Useful Life (RUL), and risk scores, into optimization models.
- Develop and deploy scalable optimization services using AWS cloud technologies.
- Build REST APIs and containerized Python services to integrate optimization engines with enterprise applications.
- Support infrastructure automation using Terraform and CI/CD pipelines.
- Develop automated tests for model constraints, reproducibility, regression, and historical solution comparisons.
- Explain optimization results to business stakeholders, including selected projects, deferred investments, binding constraints, and financial tradeoffs.
- Document mathematical formulations, assumptions, model limitations, and technical decisions for auditability and future maintenance.
Required Technical Skills
- Mixed-Integer Linear Programming (MILP)
- Operations Research and Mathematical Optimization
- Strong Python programming
- Hands-on experience with optimization frameworks such as:
- Pyomo
- PuLP
- OR-Tools
- Gurobi Python API
- Experience with optimization solvers such as:
- Strong understanding of linear and integer programming, decision variables, objective functions, and constraints.
- Experience with knapsack-style optimization, resource-constrained scheduling, and multi-period planning.
- Understanding of binary selection variables, logical constraints, Big-M formulations, and project dependencies.
- Experience with multi-objective or weighted-objective optimization and tradeoff analysis.
- Ability to troubleshoot infeasible, unbounded, or slow optimization models.
- Familiarity with sensitivity analysis, dual values, and shadow prices.
- Understanding of deterministic and reproducible solver-based systems.
Cloud & Software Engineering Skills
- Experience developing applications or services using AWS, Azure, or similar cloud platforms.
- Familiarity with AWS services such as:
- AWS Batch
- Fargate / ECS
- Lambda
- Step Functions
- S3
- RDS
- SageMaker
- Experience with Python-based APIs, serverless applications, containers, and distributed systems.
- Familiarity with Terraform or other Infrastructure as Code tools.
- Experience with Git, GitHub, CI/CD, and automated testing.
- Experience preparing, validating, and managing large datasets for optimization models.
- Understanding of relational and cloud-based databases, data modeling, queries, and application integration.
- Bachelor's or Master's degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Information Technology, or a related field.
- 5+ years of relevant professional experience.
- Experience building or supporting production optimization models or decision-support applications.
- Background in utilities, energy, power generation, infrastructure planning, or asset management.
- Experience in supply chain, logistics, airline scheduling, transportation, manufacturing, or financial portfolio optimization.
- Familiarity with financial evaluation concepts such as Present Value (PV), Net Present Value (NPV), escalation, and discounting.
- Understanding of predictive analytics outputs such as Probability of Failure, Remaining Useful Life, and risk scoring.
- Experience building reusable software components, optimization templates, and engineering frameworks.
Ideal Candidate Profile
The strongest candidate will likely come from one of these backgrounds:
- Industrial Engineer / Operations Research Engineer who transitioned into Python software development.
- Supply Chain or Logistics Optimization Engineer with experience in scheduling, routing, resource allocation, or inventory optimization.
- Utility or Energy Planning Engineer with experience in capital planning, asset management, or infrastructure investment prioritization.
- Quantitative Finance / Portfolio Optimization Engineer with experience in mathematical optimization and production software development.
Candidates from other industries, including banking, financial services, manufacturing, transportation, and logistics, are also encouraged to apply if they have hands-on MILP and Python experience.
Recruiter Screening – Critical Requirements
MILP + Python are the primary screening requirements. Do not prioritize candidates solely because they have Python, AWS, AI, or general software development experience.
Search for these keywords:
- Mixed-Integer Linear Programming (MILP)
- Operations Research
- Mathematical Optimization
- Pyomo
- PuLP
- OR-Tools
- Gurobi
- CPLEX
- HiGHS
- CBC
- Linear Programming
- Integer Programming
- Scheduling Optimization
- Network Optimization
- Portfolio Optimization
- Resource Allocation
Mandatory screening question:
“Walk me through a Mixed-Integer Linear Programming model you personally built. What were the decision variables, constraints, and objective function? Which Python framework and solver did you use?”
Candidates should be able to explain a real optimization model in detail. General statements about optimizing application performance or SQL queries do not demonstrate mathematical optimization experience.
Important: AWS, Terraform, and AI-agent experience are secondary to MILP and Python. GitHub Copilot, AI agents, and Spec-Driven Development are not mandatory prerequisites; candidates can learn these on the job.
